Breast Cancer is a severe and common disease that affects millions of women. Early detection of breast cancer, could reduce the risk that it poses to the patient. To help with early detection, we are working on a model based on ML to detect breast cancer. For the model, we take dataset taken through Fine Needle Aspiration. This Test provides the model with 32 distinct parameters such as radius, smoothness, curvature, etc. Currently we are using 569 data for the dataset of which 75% is being used to train the model. Using Logistic Regression, these parameters and the current amount of data allows for us to achieve a high accuracy of 94.4%.
Breast Cancer data analysis http://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+%28diagnostic%29
Use the DataSet and run the .py file to check out the accuracy of the program.